recipe bioconductor-indeed

Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package






An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers.

package bioconductor-indeed

(downloads) docker_bioconductor-indeed



depends r-base:


depends r-devtools:


depends r-glasso:


depends r-igraph:


depends r-visnetwork:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-indeed

and update with::

   mamba update bioconductor-indeed

To create a new environment, run:

mamba create --name myenvname bioconductor-indeed

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-indeed/tags`_ for valid values for ``<tag>``)

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